System for predictive product replacement cycling

ABSTRACT

Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to calculate product replacement cycle times and predict resource transfers associated with said product replacements. As such, the system allows for receipt of resource transfer datasets which are processed along with historical datasets via a machine learning engine. The system may then identify data trends and generate predictions of future resource transfers associated with product or service lifecycles. Furthermore, the system may also identify potential merchants or other third-party systems which may be associated with the predicted future transfers.

BACKGROUND

When a customer purchases a product or service from a merchant, it islikely that on a future date, the product or service will be purchasedagain as it will need to be replaced. On many occasions, however, thecustomer will purchase a similar product or service at a second merchantas a replacement. In this case, neither the original merchant nor thesecond merchant is aware of the other transaction, preventing eitherentity from accurately estimating the lifecycle of the product orservice. As such, a need exists for a system which can determine areplacement cycle for a product or service independently of themerchants that sell it.

BRIEF SUMMARY

The following presents a simplified summary of one or more embodimentsof the invention in order to provide a basic understanding of suchembodiments. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments, nor delineate the scope of any orall embodiments. Its sole purpose is to present some concepts of one ormore embodiments in a simplified form as a prelude to the more detaileddescription that is presented later.

Embodiments of the invention relate to systems, methods, and computerprogram products for predictive product replacement cycling, theinvention including: receiving a resource transfer dataset from amanaging entity system, where the resource transfer dataset comprisesdata associated with a first resource transfer facilitated by themanaging entity system and an identifier of a user associated with thefirst resource transfer; assigning the resource transfer dataset to acategory of dataset types, where the assigned category is selected fromone or more of a plurality of predetermined categories; querying adatabase for one or more datasets matching the assigned category andappending the resource transfer dataset to the one or more datasetsmatching the assigned category, creating a combined dataset; processingthe combined dataset via a machine learning engine to predict a futureresource transfer by the user associated with the first resourcetransfer; and transmitting a notification to the managing entity system,where the notification comprises information associated with thepredicted future resource transfer.

In some embodiments, assigning the resource transfer dataset to acategory of dataset types further includes assigning the resourcetransfer dataset to a subcategory of dataset types, where the assignedsubcategory is selected from one or more of a plurality of predeterminedsubcategories.

In some embodiments, assigning the dataset to a category of datasettypes further includes calculating a similarity score to one or more ofa plurality of predetermined categories.

In some embodiments, processing the combined dataset via a machinelearning engine further includes generating a machine learning dataset.

In some embodiments, the machine learning dataset includes dataidentifying one or more patterns or sequences of a plurality of resourcetransfers.

In some embodiments, the invention includes receiving supplementalinformation from the managing entity system, where the supplementalinformation includes data from a positioning system device associatedwith a user device and data from a managing entity applicationassociated with the user device.

In some embodiments, the data associated with the first resourcetransfer includes information identifying a merchant associated with thefirst resource transfer.

In some embodiments, predicting a future resource transfer by the userassociated with the first resource transfer further includes determininga list of one or more merchants associated with the predicted futureresource transfer.

In some embodiments, the notification includes the list of one or moremerchants associated with the predicted future resource transfer.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined with yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, wherein:

FIG. 1 illustrates an operating environment for the product replacementprediction system, in accordance with one embodiment of the presentdisclosure;

FIG. 2 is a block diagram illustrating the product replacementprediction system;

FIG. 3 is a block diagram illustrating a user device associated with theproduct replacement prediction system;

FIG. 4 is a flow diagram illustrating a process using the productreplacement prediction system, in accordance with one embodiment of thepresent disclosure; and

FIG. 5 is a flow diagram illustrating a process using the productreplacement prediction system, in accordance with another embodiment ofthe present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to elements throughout. Wherepossible, any terms expressed in the singular form herein are meant toalso include the plural form and vice versa, unless explicitly statedotherwise. Also, as used herein, the term “a” and/or “an” shall mean“one or more,” even though the phrase “one or more” is also used herein.

“Entity” or “managing entity” as used herein may refer to anyorganization, entity, or the like in the business of moving, investing,or lending money, dealing in financial instruments, or providingfinancial services. This may include commercial banks, thrifts, federaland state savings banks, savings and loan associations, credit unions,investment companies, insurance companies and the like. In someembodiments, the entity may allow a user to establish an account withthe entity. An “account” may be the relationship that the user has withthe entity. Examples of accounts include a deposit account, such as atransactional account (e.g., a banking account), a savings account, aninvestment account, a money market account, a time deposit, a demanddeposit, a pre-paid account, a credit account, or the like. The accountis associated with and/or maintained by the entity. In otherembodiments, an entity may not be a financial institution. In stillother embodiments, the entity may be the merchant itself.

“Entity system” or “managing entity system” as used herein may refer tothe computing systems, devices, software, applications, communicationshardware, and/or other resources used by the entity to perform thefunctions as described herein. Accordingly, the entity system maycomprise desktop computers, laptop computers, servers,Internet-of-Things (“IoT”) devices, networked terminals, mobilesmartphones, smart devices (e.g., smart watches), network connections,and/or other types of computing systems or devices and/or peripheralsalong with their associated applications.

“User” as used herein may refer to an individual associated with anentity. As such, in some embodiments, the user may be an individualhaving past relationships, current relationships or potential futurerelationships with an entity. In some instances, a “user” is anindividual who has a relationship with the entity, such as a customer ora prospective customer. Accordingly, as used herein the term “userdevice” or “mobile device” may refer to mobile phones, personalcomputing devices, tablet computers, wearable devices, and/or anyportable electronic device capable of receiving and/or storing datatherein and are owned, operated, or managed by a user.

“Transaction” or “resource transfer” as used herein may refer to anycommunication between a user and a third party merchant or individual totransfer funds for purchasing or selling of a product. A transaction mayrefer to a purchase of goods or services, a return of goods or services,a payment transaction, a credit transaction, or other interactioninvolving a user's account. In the context of a financial institution, atransaction may refer to one or more of: a sale of goods and/orservices, initiating an automated teller machine (ATM) or online bankingsession, an account balance inquiry, a rewards transfer, an accountmoney transfer or withdrawal, opening a bank application on a user'scomputer or mobile device, a user accessing their e-wallet, or any otherinteraction involving the user and/or the user's device that isdetectable by the financial institution. A transaction may include oneor more of the following: renting, selling, and/or leasing goods and/orservices (e.g., groceries, stamps, tickets, DVDs, vending machine items,and the like); making payments to creditors (e.g., paying monthly bills;paying federal, state, and/or local taxes; and the like); sendingremittances; loading money onto stored value cards (SVCs) and/or prepaidcards; donating to charities; and/or the like.

The system allows for use of a machine learning engine to calculateproduct replacement cycle times and therefore predict future resourcetransfers associated with said product replacements. Unlike a singlemerchant, a managing entity such as a financial institution mayfacilitate resource transfers between a single user and a largeplurality of merchants. By collecting data associated with each resourcetransfer, the system may identify data trends and generate predictionsof future resource transfers for specific products independently of themerchant at which the future transfer may take place. In this way, thesystem may benefit a number of merchants, as well as managing entities,by providing product/service insights and data analysis that would notbe obtainable by any one entity operating alone.

The system may enable, based on a customer purchasing another productrelated to a first product and a time span of the replacement cyclepassing, a managing entity to recommend that a merchant offer thecustomer a replacement for the product. As an example, the system maydetermine that after a customer purchases a coffee machine from a firstmerchant, the customer will continue to purchase coffee from that firstmerchant for twelve months, after which the customer stops purchasingcoffee from the first merchant and starts purchasing coffee from asecond merchant. The features and functions of the system will allow amanaging entity to notify both the first and second merchant of thistrend, enabling both merchants the ability to provide targetedadvertisement and product recommendations to that customer at theappropriate time. Additionally or alternatively, the system may employdata from a variety of sources to further refine transactionpredictions, including but not limited to location-based service dataand data captured during user/managing entity interactions other thanresource transfers, such as activation of reward offers.

In some embodiments of the system, the system may identify customertrends or patterns relating to combinations of product categories andsubcategories, as is further described herein. For example, the systemmay determine that a customer who purchases a coffee maker, belonging toan exemplary category of kitchen appliances, may be likely to purchase aproduct from a secondary category, such as dishware. In this example,the system may prepare a report for a managing entity identifying thisconnection and the managing entity may distribute this information toone or more merchants which sell dishware. In addition, the system maygenerate predictions of future resource transfers based on thecalculated lifecycle of one particular product. For example, the systemmay determine that the average lifecycle for a coffee maker from aparticular merchant is three years. In this example, if a customerpurchases that coffee maker on a specific date, the system may send anotification to the managing entity or merchant system on the same datethree years later in order to alert the merchant that the customer islikely ready to purchase a replacement product.

FIG. 1 illustrates an operating environment 100 for the productreplacement prediction system, in accordance with one embodiment of thepresent disclosure. As illustrated, the operating environment 100 maycomprise a user 102 and/or a user device 104 in operative communicationwith one or more third party systems 400 (e.g., third party merchantsystems). The operative communication may occur via a network 101 asdepicted, or the user 102 may be physically present at a locationassociated with the third party, such as a computer terminal orpoint-of-sale device located within a storefront. The operatingenvironment also includes a managing entity system 500, a productreplacement prediction system 200, a database 300, and/or othersystems/devices not illustrated herein and connected via a network 101.As such, the user 102 may complete a resource transfer with the thirdparty system 400 by establishing operative communication channelsbetween the user device 104, the managing entity system 500, and thethird party system 400 via a wireless network 101. In other embodiments,the user may complete a resource transfer with the third party system byinterfacing directly with the third party system 400, which may thenestablish operative communication with the managing entity system 500via a wireless network in order to execute the resource transfer.

Typically, the product replacement prediction system 200 and thedatabase 300 are in operative communication with the managing entitysystem 500, via the network 101, which may be the internet, an intranetor the like. In FIG. 1 , the network 101 may include a local areanetwork (LAN), a wide area network (WAN), a global area network (GAN),and/or near field communication (NFC) network. The network 101 mayprovide for wireline, wireless, or a combination of wireline andwireless communication between devices in the network. In someembodiments, the network 101 includes the Internet. In some embodiments,the network 101 may include a wireless telephone network. Furthermore,the network 101 may comprise wireless communication networks toestablish wireless communication channels such as a contactlesscommunication channel and a near field communication (NFC) channel (forexample, in the instances where communication channels are establishedbetween the user device 104 and the third party system 400). In thisregard, the wireless communication channel may further comprise nearfield communication (NFC), communication via radio waves, communicationthrough the internet, communication via electromagnetic waves and thelike.

The user device 104 may comprise a mobile communication device, such asa cellular telecommunications device (i.e., a smart phone or mobilephone), a computing device such as a laptop computer, a personal digitalassistant (PDA), a mobile internet accessing device, or other mobiledevice including, but not limited to portable digital assistants (PDAs),pagers, mobile televisions, laptop computers, cameras, video recorders,audio/video player, radio, GPS devices, any combination of theaforementioned, or the like. The user device is described in greaterdetail with respect to FIG. 3 .

The managing entity system 500 may comprise a communication module andmemory not illustrated, and may be configured to establish operativecommunication channels with a third party system 400 and/or a userdevice 104 via a network 101. The managing entity may comprise a userdata repository which stores user account data. This data may be used bythe managing entity to facilitate resource transfers between the user102 or user device 104 and third party system 400. In some embodiments,the managing entity system is in operative communication with theproduct replacement prediction system 200 and database 300 via a privatecommunication channel. The private communication channel may be via anetwork 101 or the product replacement prediction system 200 anddatabase 300 may be fully integrated within the managing entity system500.

As will be discussed in greater detail in FIG. 4 and FIG. 5 , themanaging entity system 500 may communicate with the product replacementprediction system 200 in order to transmit data associated with resourcetransfers between a plurality of users 102 and a plurality of thirdparty systems 400. In some embodiments, the managing entity may utilizethe features and functions of the product replacement prediction systemto predict user behavior and anticipate future resource transfers. Inother embodiments, the managing entity and/or the one or more thirdparty systems may utilize the product replacement prediction system toreact to identified trends in user behavior.

FIG. 2 illustrates a block diagram of the product replacement predictionsystem 200 associated with the operating environment 100, in accordancewith embodiments of the present invention. As illustrated in FIG. 2 ,the product replacement prediction system 200 may include acommunication device 244, a processing device 242, and a memory device250 having a prediction application/module 253, a processing systemapplication 254 and a processing system datastore 255 stored therein. Asshown, the processing device 242 is operatively connected to and isconfigured to control and cause the communication device 244, and thememory device 250 to perform one or more functions. In some embodiments,the prediction module 253 and/or the processing system application 254comprises computer readable instructions that when executed by theprocessing device 242 cause the processing device 242 to perform one ormore functions and/or transmit control instructions to the database 300,the managing entity system 500, and/or the communication device 244. Itwill be understood that the prediction module 253 and/or the processingsystem application 254 may be executable to initiate, perform, complete,and/or facilitate one or more portions of any embodiments describedand/or contemplated herein. The prediction module 253 may compriseexecutable instructions associated with data processing and analysisrelated to resource transfer data and may be embodied within theprocessing system application 254 in some instances. The productreplacement prediction system 200 may be owned by, operated by and/oraffiliated with the same managing entity that owns or operates themanaging entity system 500. In some embodiments, the product replacementprediction system 200 is fully integrated within the managing entitysystem 500.

The prediction module 253 may further comprise a data analysis module260, a machine learning engine 261, and a machine learning dataset(s)262. The data analysis module 260 may store instructions and/or datathat may cause or enable the product replacement prediction system 200to receive, store, and/or analyze data received by the managing entitysystem 500 or the database 300. The data analysis module may processdata to identify product categories and subcategories as will be furtherdiscussed in FIG. 4 . The machine learning engine 261 and machinelearning dataset(s) 262 may store instructions and/or data that cause orenable the product replacement prediction system 200 to determine, inreal-time and based on received information, a predicted lifecycle oruse cycle of a particular product or service. The machine learningdataset(s) 262 may contain data queried from database 300 and/or may bebased on historical data relating to a particular product category,user, third party merchant, or the like. In some embodiments, themachine learning dataset(s) 262 may also contain data relating to useractivity other than resource transfers as is further described herein.

The machine learning engine 261 may receive data from a plurality ofsources and, using one or more machine learning algorithms, may generateone or more machine learning datasets 262. Various machine learningalgorithms may be used without departing from the invention, such assupervised learning algorithms, unsupervised learning algorithms,regression algorithms (e.g., linear regression, logistic regression, andthe like), instance based algorithms (e.g., learning vectorquantization, locally weighted learning, and the like), regularizationalgorithms (e.g., ridge regression, least-angle regression, and thelike), decision tree algorithms, Bayesian algorithms, clusteringalgorithms, artificial neural network algorithms, and the like.Additional or alternative machine learning algorithms may be usedwithout departing from the invention.

The machine learning datasets 262 may include machine learning datalinking one or more details of a resource transfer (e.g. productcategory or subcategory, location purchased, merchant identifier, or thelike) with a time and/or day or date of the transfer to identify one ormore patterns or sequences of transfers that may aid in predicting oneor more future transfers by the same user or by another user with asimilar transaction history. For instance, the machine learning datasets262 may include data linking a particular purchase of a given product ona particular date to an additional purchase of a second, related producton another particular date. Thus, this data may enable the productreplacement prediction system 200 to predict a likely future transferbetween a user and a third party. The data associated with a resourcetransfer may be supplemented by additional data obtained from aninteraction between the user device 104 and the managing entity system500. For example, in some embodiments, the system may determine, basedon location data obtained from a positioning system 320 of a user device104, that a user is in closer proximity to a first third party merchantthan a second third party merchant. The product replacement predictionsystem 200 may weight that information accordingly to determine that thepredicted resource transfer is more likely to occur at the first thirdparty merchant than the second third party merchant. Additionally oralternatively, the system may determine, based on information obtainedfrom a rewards application 352 of a user device 104, that a user isinterested in or intending to interact with a particular third partymerchant. As with location-based data, the product replacementprediction system 200 may weight the information to determine that thepredicted resource transfer is more likely to occur at a third partymerchant if a user has indicated interest in that particular third partymerchant.

The communication device 244 may generally include a modem, server,transceiver, and/or other devices for communicating with other deviceson the network 101. The communication device 244 may be a communicationinterface having one or more communication devices configured tocommunicate with one or more other devices on the network 101, such asthe product replacement prediction system 200, the user device 104,other processing systems, data systems, etc.

Additionally, referring to product replacement prediction system 200illustrated in FIG. 2 , the processing device 242 may generally refer toa device or combination of devices having circuitry used forimplementing the communication and/or logic functions of the productreplacement prediction system 200. For example, the processing device242 may include a control unit, a digital signal processor device, amicroprocessor device, and various analog-to-digital converters,digital-to-analog converters, and other support circuits and/orcombinations of the foregoing. Control and signal processing functionsof the product replacement prediction system 200 may be allocatedbetween these processing devices according to their respectivecapabilities. The processing device 242 may further includefunctionality to operate one or more software programs based oncomputer-executable program code 252 thereof, which may be stored in amemory device 250, such as the processing system application 254 and theprediction module 253. As the phrase is used herein, a processing devicemay be “configured to” perform a certain function in a variety of ways,including, for example, by having one or more general-purpose circuitsperform the function by executing particular computer-executable programcode embodied in computer-readable medium, and/or by having one or moreapplication-specific circuits perform the function. The processingdevice 242 may be configured to use the network communication interfaceof the communication device 244 to transmit and/or receive data and/orcommands to and/or from the other devices/systems connected to thenetwork 101.

The memory device 250 within the product replacement prediction system200 may generally refer to a device or combination of devices that storeone or more forms of computer-readable media for storing data and/orcomputer-executable program code/instructions. For example, the memorydevice 250 may include any computer memory that provides an actual orvirtual space to temporarily or permanently store data and/or commandsprovided to the processing device 242 when it carries out its functionsdescribed herein.

FIG. 3 illustrates a block diagram of the user device associated withthe product replacement prediction system, in accordance withembodiments of the present invention. The user device 104 may include auser mobile device or the like. A “mobile device” 104 may be any mobilecommunication device, such as a cellular telecommunications device(i.e., a cell phone or mobile phone), personal digital assistant (PDA),a mobile Internet accessing device, or another mobile device including,but not limited to portable digital assistants (PDAs), pagers, mobiletelevisions, laptop computers, cameras, video recorders, audio/videoplayer, radio, GPS devices, any combination of the aforementioneddevices.

The mobile device 104 may generally include a processing device orprocessor 310 communicably coupled to devices such as, a memory device350, user output devices 340 (for example, a user display or a speaker),user input devices 330 (such as a microphone, keypad, touchpad, touchscreen, and the like), a communication device or network interfacedevice 360, a positioning system device 320, such as a geo-positioningsystem device like a GPS device, an accelerometer, and the like, one ormore chips, and the like.

The processor 310 may include functionality to operate one or moresoftware programs or applications, which may be stored in the memorydevice 320. For example, the processor 310 may be capable of operatingapplications such as a resource transfer application 351, a rewardsapplication or managing entity application 352, or a web browserapplication. The resource transfer application may then allow the userdevice 104 to transmit and receive data and instructions to or from thethird party system 300 and the rewards application 352 may allow theuser device 104 to transmit and receive data to or from the managingentity system 500 (for example, via wireless communication or NFCchannels), data and instructions to or from the processing system 200,web content, such as, for example, location-based content and/or otherweb page content, according to a Wireless Application Protocol (WAP),Hypertext Transfer Protocol (HTTP), and/or the like. The rewardsapplication 352 may allow the managing entity 500 to present the user102 with a plurality of third party merchants for which the managingentity may be offering rewards opportunities, promotions, and/or thelike for the user.

The processor 310 may be configured to use the communication device 360to communicate with one or more devices on a network 101 such as, butnot limited to the third party system 400 and the managing entity system500. In this regard the processor 310 may be configured to providesignals to and receive signals from the communication device 360. Thesignals may include signaling information in accordance with the airinterface standard of the applicable BLE standard, cellular system ofthe wireless telephone network and the like, that may be part of thenetwork 101. In this regard, the user device 104 may be configured tooperate with one or more air interface standards, communicationprotocols, modulation types, and access types. By way of illustration,the user device 104 may be configured to operate in accordance with anyof a number of first, second, third, and/or fourth-generationcommunication protocols and/or the like. For example, the mobile device104 may be configured to operate in accordance with second-generation(2G) wireless communication protocols IS-136 (time division multipleaccess (TDMA)), GSM (global system for mobile communication), and/orIS-95 (code division multiple access (CDMA)), or with third-generation(3G) wireless communication protocols, such as Universal MobileTelecommunications System (UMTS), CDMA2000, wideband CDMA (WCDMA) and/ortime division-synchronous CDMA (TD-SCDMA), with fourth-generation (4G)wireless communication protocols, and/or the like. The user device 104may also be configured to operate in accordance with non-cellularcommunication mechanisms, such as via a wireless local area network(WLAN) or other communication/data networks. The user device 104 mayalso be configured to operate in accordance Bluetooth® low energy, audiofrequency, ultrasound frequency, or other communication/data networks.

The communication device 360 may also include a user activity interfacepresented in user output devices 340 in order to allow a user 102 toexecute some or all of the processes described herein. The applicationinterface may have the ability to connect to and communicate with anexternal data storage on a separate system within the network 101. Theuser output devices 340 may include a display (e.g., a liquid crystaldisplay (LCD) or the like) and a speaker 334 or other audio device,which are operatively coupled to the processor 310. The user inputdevices 330, which may allow the user device 104 to receive data fromthe user 102, may include any of a number of devices allowing the userdevice 104 to receive data from a user 102, such as a keypad, keyboard,touch-screen, touchpad, microphone, mouse, joystick, other pointerdevice, button, soft key, and/or other input device(s).

The user device 104 may also include a memory buffer, cache memory ortemporary memory device 350 operatively coupled to the processor 310.Typically, one or more applications 351 and 352, are loaded into thetemporarily memory during use. As used herein, memory may include anycomputer readable medium configured to store data, code, or otherinformation. The memory device 350 may include volatile memory, such asvolatile Random Access Memory (RAM) including a cache area for thetemporary storage of data. The memory device 420 may also includenon-volatile memory, which can be embedded and/or may be removable. Thenon-volatile memory may additionally or alternatively include anelectrically erasable programmable read-only memory (EEPROM), flashmemory or the like.

In some instances, various features and functions of the invention aredescribed herein with respect to a “system.” In some instances, thesystem may refer to the product replacement prediction system 200performing one or more steps described herein in conjunction with otherdevices and systems, either automatically based on executing computerreadable instructions of the memory device 250, or in response toreceiving control instructions from the managing entity system 500. Insome instances, the system refers to the devices and systems on theoperating environment 100 of FIG. 1 . The features and functions ofvarious embodiments of the invention are be described below in furtherdetail.

It is understood that the servers, systems, and devices described hereinillustrate one embodiment of the invention. It is further understoodthat one or more of the servers, systems, and devices can be combined inother embodiments and still function in the same or similar way as theembodiments described herein.

FIG. 4 is a high-level process flow diagram illustrating a process usingthe product replacement prediction system, in accordance with oneembodiment of the present disclosure. The process begins at block 600,where the system receives a data packet from a managing entity system500 containing resource transfer information. The resource transferinformation contained within the data packet may include but is notlimited to data such as time, location, description of theproduct/service, resource amount, resource instrument or account used tocomplete the transfer, information identifying the merchant or thirdparty, and/or information identifying the user or customer. In someembodiments the system may receive a unique data packet after theoccurrence of an individual transfer, or in other embodiments themanaging entity may choose to group data packets together and transferthe information after a predetermined amount of time, such as once perday. In some embodiments, the system may also receive data from thepositioning system device 320 and the reward application 352 of the userdevice 104. This data may include information such as user proximity toone or more third party systems, user selections of third partymerchants for which the managing entity is offering salesencouragements, and other information which indicates a user's potentialinterest in a particular third party merchant.

The process may then continue to block 610, wherein for each individualresource transfer, the system assigns a product/service category and/orsubcategory to the resource transfer information set (e.g., via the dataanalysis module 260). In some embodiments, the categories may beassigned based on a calculated similarity score to one of a plurality ofpredetermined categories associated with the merchant identified in theresource transfer.

The process may then continue to block 620, wherein the system may querythe database 300 for datasets related to, in some embodiments, the sameuser and product category. In some embodiments, the system may query fora larger selection of users, such as users within a particulargeographic area or the like. The system may then append the resourcetransfer dataset to the queried data 630 and process the combined datavia the machine learning engine 261.

In block 640 of FIG. 4 , the output of the machine learning engine is anewly generated machine learning dataset 262. As previously described,the newly generated machine learning dataset may be used to link aparticular purchase of a given product on a particular date to anadditional purchase of a second, related product on another particulardate. This data may enable the system to predict a likely futuretransfer between a user and a third party as shown in block 650. Inblock 660 of FIG. 4 , the system may transmit a notification to themanaging entity system, wherein the notification may contain predictionsgenerated by the product replacement prediction system 200. In someembodiments, the system may transmit this notification in the form of aregularly generated report. Additionally, or alternatively, the systemmay transmit this notification in response to a query from the managingentity.

FIG. 5 is a high-level process flow diagram illustrating a process usingthe product replacement prediction system, in accordance with anotherembodiment of the present disclosure. The process begins at block 700,wherein the system predicts that a particular resource transfer willoccur. As previously discussed, the system may utilize the machinelearning engine 261 to determine that a user will purchase a product orservice on a future date based on that user's historical data, thehistorical data of similar users, and/or the like. The system may thenquery the database 300 to determine a list of one or more merchants thatmay be involved in the predicted resource transfer, as shown in block710. In some embodiments, the system may query for all merchants whichhave sold the identified product/service in the past, merchants that theuser has engaged with previously, merchants in a particular geographiclocation, and/or some combination of like information. In someembodiments, the system may use additional information from thepositioning system device 320 and the reward application 352 of the userdevice 104 to identify merchants in close proximity to the user as wellas merchants which the user has expressed interest in via the rewardapplication 352. The system may utilize the machine learning engine 261to calculate the degree to which the additional information mayinfluence the list of potential merchants and then weight the listaccording to the probability of each merchant being selected for thefuture transaction.

The process continues in block 720, wherein the system may generate amessage or data packet containing details of the predicted transfer. Themessage may contain information such as the product/service to bepurchased, a predicted time and/or location of the transfer, theresource instrument and/or account to be utilized by the user,information identifying the user, and/or the list of potential merchantsdetermined in block 710. In some embodiments, the message may comprisemultiple reports which the managing entity system may then distribute toindividual third party systems, wherein each report contains onlyinformation identifying a single third party merchant. The process iscompleted in block 730, wherein the system transmits that message to themanaging entity system.

As will be appreciated by one of ordinary skill in the art, the presentinvention may be embodied as an apparatus (including, for example, asystem, a machine, a device, a computer program product, and/or thelike), as a method (including, for example, a business process, acomputer-implemented process, and/or the like), or as any combination ofthe foregoing. Accordingly, embodiments of the present invention maytake the form of an entirely software embodiment (including firmware,resident software, micro-code, and the like), an entirely hardwareembodiment, or an embodiment combining software and hardware aspectsthat may generally be referred to herein as a “system.” Furthermore,embodiments of the present invention may take the form of a computerprogram product that includes a computer-readable storage medium havingcomputer-executable program code portions stored therein.

As the phrase is used herein, a processor may be “configured to” performa certain function in a variety of ways, including, for example, byhaving one or more general-purpose circuits perform the function byexecuting particular computer-executable program code embodied incomputer-readable medium, and/or by having one or moreapplication-specific circuits perform the function.

It will be understood that any suitable computer-readable medium may beutilized. The computer-readable medium may include, but is not limitedto, a non-transitory computer-readable medium, such as a tangibleelectronic, magnetic, optical, infrared, electromagnetic, and/orsemiconductor system, apparatus, and/or device. For example, in someembodiments, the non-transitory computer-readable medium includes atangible medium such as a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EEPROM or Flash memory), a compact discread-only memory (CD-ROM), and/or some other tangible optical and/ormagnetic storage device. In other embodiments of the present invention,however, the computer-readable medium may be transitory, such as apropagation signal including computer-executable program code portionsembodied therein.

It will also be understood that one or more computer-executable programcode portions for carrying out the specialized operations of the presentinvention may be required on the specialized computer includeobject-oriented, scripted, and/or unscripted programming languages, suchas, for example, Java, Perl, Smalltalk, C++, SQL, Python, Objective C,and/or the like. In some embodiments, the one or morecomputer-executable program code portions for carrying out operations ofembodiments of the present invention are written in conventionalprocedural programming languages, such as the “C” programming languagesand/or similar programming languages. The computer program code mayalternatively or additionally be written in one or more multi-paradigmprogramming languages, such as, for example, F #.

Embodiments of the present invention are described above with referenceto flowcharts and/or block diagrams. It will be understood that steps ofthe processes described herein may be performed in orders different thanthose illustrated in the flowcharts. In other words, the processesrepresented by the blocks of a flowchart may, in some embodiments, be inperformed in an order other that the order illustrated, may be combinedor divided, or may be performed simultaneously. It will also beunderstood that the blocks of the block diagrams illustrated, in someembodiments, merely conceptual delineations between systems and one ormore of the systems illustrated by a block in the block diagrams may becombined or share hardware and/or software with another one or more ofthe systems illustrated by a block in the block diagrams. Likewise, adevice, system, apparatus, and/or the like may be made up of one or moredevices, systems, apparatuses, and/or the like. For example, where aprocessor is illustrated or described herein, the processor may be madeup of a plurality of microprocessors or other processing devices whichmay or may not be coupled to one another. Likewise, where a memory isillustrated or described herein, the memory may be made up of aplurality of memory devices which may or may not be coupled to oneanother.

It will also be understood that the one or more computer-executableprogram code portions may be stored in a transitory or non-transitorycomputer-readable medium (e.g., a memory, and the like) that can directa computer and/or other programmable data processing apparatus tofunction in a particular manner, such that the computer-executableprogram code portions stored in the computer-readable medium produce anarticle of manufacture, including instruction mechanisms which implementthe steps and/or functions specified in the flowchart(s) and/or blockdiagram block(s).

The one or more computer-executable program code portions may also beloaded onto a computer and/or other programmable data processingapparatus to cause a series of operational steps to be performed on thecomputer and/or other programmable apparatus. In some embodiments, thisproduces a computer-implemented process such that the one or morecomputer-executable program code portions which execute on the computerand/or other programmable apparatus provide operational steps toimplement the steps specified in the flowchart(s) and/or the functionsspecified in the block diagram block(s). Alternatively,computer-implemented steps may be combined with operator and/orhuman-implemented steps in order to carry out an embodiment of thepresent invention.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations and modifications ofthe just described embodiments can be configured without departing fromthe scope and spirit of the invention. Therefore, it is to be understoodthat, within the scope of the appended claims, the invention may bepracticed other than as specifically described herein.

What is claimed is:
 1. A system for predictive product replacementcycling, the system comprising: at least one non-transitory storagedevice; and at least one processing device coupled to the at least onenon-transitory storage device, wherein the at least one processingdevice is configured to: receive, via a wireless communication link, aresource transfer dataset from a managing entity system, wherein theresource transfer dataset comprises data associated with a firstresource transfer facilitated by the managing entity system and anidentifier of a user associated with the first resource transfer;receive, via a wireless communication link, supplemental informationfrom a user device, wherein the supplemental information comprises datafrom a positioning system of the user device and one or more user inputsassociated with a managing entity application installed on the userdevice; calculate, for each of a plurality of predetermined categoriesof dataset types, a similarity score of the first resource transfer tothe predetermined category; assign the resource transfer dataset to thepredetermined category having the highest similarity score; query, basedon the data from the positioning system of the user device, a databasefor one or more datasets matching the assigned category and appendingthe resource transfer dataset to the one or more datasets matching theassigned category, creating a combined dataset; identify, based on thedata from the positioning system of the user device, at least one thirdparty merchant system within a predetermined proximity of the userdevice; process the combined dataset, information associated with the atleast one third party merchant system, and the supplemental informationvia a machine learning engine to predict a future resource transfer bythe user associated with the first resource transfer, wherein themachine learning engine is configured to assign weights to the one ormore user inputs associated with the managing entity applicationinstalled on the user device; and transmit a notification to themanaging entity system, wherein the notification comprises informationassociated with the predicted future resource transfer.
 2. The system ofclaim 1, wherein the at least one processing device is furtherconfigured to, when assigning the resource transfer dataset to thecategory of dataset types, assign the resource transfer dataset to asubcategory of dataset types, wherein the assigned subcategory isselected from one or more of a plurality of predetermined subcategories.3. The system of claim 1, wherein the at least one processing device isfurther configured to, when assigning the resource transfer dataset tothe category of dataset types, assign a similarity score to one or moreof a plurality of predetermined categories.
 4. The system of claim 1,wherein the at least one processing device is further configured to,when processing the combined dataset via the machine learning engine,generate a machine learning dataset, wherein the machine learningdataset comprises data identifying one or more patterns or sequences ofa plurality of resource transfers.
 5. The system of claim 1, wherein thedata associated with the first resource transfer comprises informationidentifying a merchant associated with the first resource transfer. 6.The system of claim 5, wherein the at least one processing device isfurther configured to, when processing the combined dataset via themachine learning engine to predict the future resource transfer by theuser associated with the first resource transfer, determine a list ofone or more merchants associated with the predicted future resourcetransfer.
 7. The system of claim 6, wherein the notification furthercomprises the list of one or more merchants associated with thepredicted future resource transfer.
 8. A computer program product forpredictive product replacement cycling, the computer program productcomprising at least one non-transitory computer-readable medium havingcomputer-readable program code portions embodied therein, thecomputer-readable program code portions comprising: an executableportion configured for receiving, via a wireless communication link, aresource transfer dataset from a managing entity system, wherein theresource transfer dataset comprises data associated with a firstresource transfer facilitated by the managing entity system and anidentifier of a user associated with the first resource transfer; anexecutable portion configured for receiving, via a wirelesscommunication link, supplemental information from a user device, whereinthe supplemental information comprises data from a positioning system ofthe user device and one or more user inputs associated with a managingentity application installed on the user device; an executable portionconfigured for calculating, for each of a plurality of predeterminedcategories of dataset types, a similarity score of the first resourcetransfer to the predetermined category; an executable portion configuredfor assigning the resource transfer dataset to the predeterminedcategory having the highest similarity score; an executable portionconfigured for querying, based on the data from the positioning systemof the user device, a database for one or more datasets matching theassigned category and appending the resource transfer dataset to the oneor more datasets matching the assigned category, creating a combineddataset; an executable portion configured for identifying, based on thedata from the positioning system of the user device, at least one thirdparty merchant system within a predetermined proximity of the userdevice; an executable portion configured for processing the combineddataset, information associated with the at least one third partymerchant system, and the supplemental information via a machine learningengine to predict a future resource transfer by the user associated withthe first resource transfer, wherein the machine learning engine isconfigured to assign weights to the one or more user inputs associatedwith the managing entity application installed on the user device; andan executable portion configured for transmitting a notification to themanaging entity system, wherein the notification comprises informationassociated with the predicted future resource transfer.
 9. The computerprogram product of claim 8, wherein assigning the resource transferdataset to the category of dataset types further comprises assigning theresource transfer dataset to a subcategory of dataset types, wherein theassigned subcategory is selected from one or more of a plurality ofpredetermined subcategories.
 10. The computer program product of claim8, wherein assigning the resource transfer dataset to the category ofdataset types further comprises calculating a similarity score to one ormore of a plurality of predetermined categories.
 11. The computerprogram product of claim 8, wherein processing the combined dataset viathe machine learning engine further comprises generating a machinelearning dataset, wherein the machine learning dataset comprises dataidentifying one or more patterns or sequences of a plurality of resourcetransfers.
 12. The computer program product of claim 8, wherein the dataassociated with the first resource transfer comprises informationidentifying a merchant associated with the first resource transfer. 13.The computer program product of claim 12, wherein processing thecombined dataset via the machine learning engine to predict the futureresource transfer by the user associated with the first resourcetransfer further comprises determining a list of one or more merchantsassociated with the predicted future resource transfer.
 14. The computerprogram product of claim 13, wherein the notification further comprisesthe list of one or more merchants associated with the predicted futureresource transfer.
 15. A computer-implemented method for predictiveproduct replacement cycling, the method comprising: providing acomputing system comprising a computer processing device and anon-transitory computer readable medium, wherein the computer readablemedium comprises configured computer program instruction code, such thatwhen said instruction code is operated by said computer processingdevice, said computer processing device performs the followingoperations: receiving, via a wireless communication link, a resourcetransfer dataset from a managing entity system, wherein the resourcetransfer dataset comprises data associated with a first resourcetransfer facilitated by the managing entity system and an identifier ofa user associated with the first resource transfer; receiving, via awireless communication link, supplemental information from a userdevice, wherein the supplemental information comprises data from apositioning system of the user device and one or more user inputsassociated with a managing entity application installed on the userdevice; calculating, for each of a plurality of predetermined categoriesof dataset types, a similarity score of the first resource transfer tothe predetermined category; assigning the resource transfer dataset tothe predetermined category having the highest similarity score;querying, based on the data from the positioning system of the userdevice, a database for one or more datasets matching the assignedcategory and appending the resource transfer dataset to the one or moredatasets matching the assigned category, creating a combined dataset;identifying, based on the data from the positioning system of the userdevice, at least one third party merchant system within a predeterminedproximity of the user device; processing the combined dataset,information associated with the at least one third party merchantsystem, and the supplemental information via a machine learning engineto predict a future resource transfer by the user associated with thefirst resource transfer, wherein the machine learning engine isconfigured to assign weights to the one or more user inputs associatedwith the managing entity application installed on the user device; andtransmitting a notification to the managing entity system, wherein thenotification comprises information associated with the predicted futureresource transfer.
 16. The computer-implemented method of claim 15,wherein assigning the resource transfer dataset to the category ofdataset types further comprises calculating a similarity score to one ormore of a plurality of predetermined categories.
 17. Thecomputer-implemented method of claim 16, wherein processing the combineddataset via the machine learning engine further comprises generating amachine learning dataset, wherein the machine learning dataset comprisesdata identifying one or more patterns or sequences of a plurality ofresource transfers.
 18. The computer-implemented method of claim 15,wherein the data associated with the first resource transfer comprisesinformation identifying a merchant associated with the first resourcetransfer, and wherein processing the combined dataset via a machinelearning engine to predict the future resource transfer by the userassociated with the first resource transfer further comprisesdetermining a list of one or more merchants associated with thepredicted future resource transfer, and wherein the notificationcomprises the list of one or more merchants associated with thepredicted future resource transfer.